refactor(diann): Integrate MSstatsImport and MSstatsClean functions into DIANN converter

NAMESPACE

@@ -7,5 +7,7 @@ export(bigFragPipetoMSstatsFormat)
 export(bigSpectronauttoMSstatsFormat)
 importFrom(MSstats,dataProcess)
 importFrom(MSstats,groupComparison)
+importFrom(MSstatsConvert,MSstatsClean)
+importFrom(MSstatsConvert,MSstatsImport)
 importFrom(utils,head)
 importFrom(utils,sessionInfo)

R/clean_DIANN.R

@@ -10,7 +10,7 @@
 #' @return NULL. Writes to file.
 #' @keywords internal
 reduceBigDIANN <- function(input_file, output_path, MBR = TRUE,
-                           quantificationColumn = "Fragment.Quant.Corrected",
+                           quantificationColumn = "FragmentQuantCorrected",
                            global_qvalue_cutoff = 0.01,
                            qvalue_cutoff = 0.01,
                            pg_qvalue_cutoff = 0.01) {
@@ -22,14 +22,9 @@ reduceBigDIANN <- function(input_file, output_path, MBR = TRUE,
     delim <- ";"
   }
 
-  diann_chunk <- function(x, pos) cleanDIANNChunk(x,
-                                                  output_path,
-                                                  MBR,
-                                                  quantificationColumn,
-                                                  pos,
-                                                  global_qvalue_cutoff,
-                                                  qvalue_cutoff,
-                                                  pg_qvalue_cutoff)
+  diann_chunk <- function(x, pos) cleanDIANNChunk(x, output_path, MBR, quantificationColumn, pos,
+                     global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)
+
   readr::read_delim_chunked(input_file,
                             readr::DataFrameCallback$new(diann_chunk),
                             delim = delim,
@@ -46,245 +41,19 @@ reduceBigDIANN <- function(input_file, output_path, MBR = TRUE,
 #' @param global_qvalue_cutoff Global Q-value cutoff
 #' @param qvalue_cutoff Q-value cutoff
 #' @param pg_qvalue_cutoff Protein group Q-value cutoff
+#' @importFrom MSstatsConvert MSstatsImport MSstatsClean
 #' @return NULL
 #' @keywords internal
 cleanDIANNChunk = function(input, output_path, MBR, quantificationColumn, pos,
                            global_qvalue_cutoff = 0.01,
                            qvalue_cutoff = 0.01,
                            pg_qvalue_cutoff = 0.01) {
-  # 1. Handle "auto" quantification column
-  processed <- .handleAutoQuantification(input, quantificationColumn)
-  input <- processed$input
-  quantificationColumn <- processed$quantificationColumn
-
-  # 2. Select required columns
-  input <- .selectDIANNColumns(input, MBR, quantificationColumn)
-  input <- .cleanDIANNAddMissingColumns(input)
-
-  # 3. Expand concatenated rows
-  input <- .expandDIANNRows(input, quantificationColumn)
-
-  # 4. Process fragment info (extract intensity, charge, ion)
-  input <- .processDIANNFragmentInfo(input, quantificationColumn)
-
-  # 5. Filter invalid fragments
-  input <- .filterDIANNFragments(input, quantificationColumn)
-
-  # 6. Standardize column names
-  input <- .standardizeDIANNColumns(input, quantificationColumn)
-
-  # 7. Filter by Q-values
-  input <- .filterDIANNByQValues(input, MBR, global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)
-
-  # 8. Finalize columns (select final set, add IsotopeLabelType)
-  input <- .finalizeDIANNColumns(input)
-
-  # 9. Write to output
-  .writeDIANNChunk(input, output_path, pos)
-
-  NULL
-}
-
-#' Handle automatic detection of quantification columns
-#' 
-#' @param input Data frame
-#' @param quantificationColumn Name of column or "auto"
-#' @return List with input data frame and updated quantification column name
-#' @keywords internal
-.handleAutoQuantification <- function(input, quantificationColumn) {
-  if (quantificationColumn == "auto") {
-    fragment_columns <- grep("^Fr[0-9]+Quantity$", colnames(input), value = TRUE)
-    if (length(fragment_columns) == 0) {
-      stop("No fragment quantification columns found. Please check your input.")
-    }
-    input <- tidyr::unite(input, "FragmentQuantCorrected", all_of(fragment_columns), sep = ";")
-    quantificationColumn <- "FragmentQuantCorrected"
-  }
-  list(input = input, quantificationColumn = quantificationColumn)
-}
-
-#' Select required columns from DIANN output
-#' 
-#' @param input Data frame
-#' @param MBR Boolean
-#' @param quantificationColumn Name of intensity column
-#' @return Data frame with selected columns
-#' @keywords internal
-.selectDIANNColumns <- function(input, MBR, quantificationColumn) {
-  base_cols <- c('Protein.Names', 'Stripped.Sequence', 'Modified.Sequence', 
-                 'Precursor.Charge', quantificationColumn, 'Q.Value', 
-                 'Precursor.Mz', 'Fragment.Info', 'Run')
-
-  mbr_cols <- if (MBR) {
-    c('Lib.Q.Value', 'Lib.PG.Q.Value')
-  } else {
-    c('Global.Q.Value', 'Global.PG.Q.Value')
-  }
-
-  req_cols <- intersect(c(base_cols, mbr_cols), colnames(input))
-  dplyr::select(input, all_of(req_cols))
-}
-
-#' Add missing required columns
-#' 
-#' @param input Data frame
-#' @return Data frame with missing columns added
-#' @keywords internal
-.cleanDIANNAddMissingColumns <- function(input) {
-  if (!"Precursor.Mz" %in% colnames(input)) {
-    input <- dplyr::mutate(input, Precursor.Mz = NA)
-  }
-  if (!"Fragment.Info" %in% colnames(input)) {
-    input <- dplyr::mutate(input, Fragment.Info = NA)
-  }
-  input
-}
-
-#' Expand rows with multiple fragments
-#' 
-#' @param input Data frame
-#' @param quantificationColumn Name of intensity column
-#' @return Data frame with expanded rows
-#' @keywords internal
-.expandDIANNRows <- function(input, quantificationColumn) {
-  split_cols <- intersect(c(quantificationColumn, "Fragment.Info"), colnames(input))
-  if (length(split_cols) > 0) {
-    tidyr::separate_rows(input, all_of(split_cols), sep = ";")
-  } else {
-    input
-  }
-}
-
-#' Process fragment information strings
-#' 
-#' @param input Data frame
-#' @param quantificationColumn Name of intensity column
-#' @return Data frame with FragmentIon and ProductCharge columns
-#' @keywords internal
-.processDIANNFragmentInfo <- function(input, quantificationColumn) {
-  # Convert Intensity to Numeric from Char strings
-  input[[quantificationColumn]] <- as.numeric(input[[quantificationColumn]])
-
-  # Generate fragment info if missing
-  if (all(is.na(input$Fragment.Info))) {
-    input <- dplyr::group_by(input, Protein.Names, Modified.Sequence, Precursor.Charge, Run)
-    input <- dplyr::mutate(input, Fragment.Info = paste0("Frag", dplyr::row_number()))
-    input <- dplyr::ungroup(input)
-  }
-
-  dplyr::mutate(
-    input,
-    FragmentIon = sub('\\^\\.\\*', '', .data$Fragment.Info),
-
-    # Extract product charge
-    ProductCharge = dplyr::if_else(
-      grepl("/", .data$Fragment.Info),
-      # Extract charge (number right after "/" in string), default to 1 if parsing fails
-      as.integer(stringr::str_extract(.data$Fragment.Info, "(?<=/)[0-9]+")),
-      1L,
-      missing = 1L
+    input = MSstatsImport(list(input = input),
+                                          "MSstats", "DIANN")
+    input = MSstatsClean(
+        input, MBR, quantificationColumn
+        #Todo: Add , global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff params
     )
-  )
-}
-
-#' Filter invalid fragments
-#' 
-#' @param input Data frame
-#' @param quantificationColumn Name of intensity column
-#' @return Filtered data frame
-#' @keywords internal
-.filterDIANNFragments <- function(input, quantificationColumn) {
-  dplyr::filter(
-    input,
-    (!grepl("NH3|H2O", .data$FragmentIon) | is.na(.data$FragmentIon)) & !is.na(.data[[quantificationColumn]])
-  )
-}
-
-#' Standardize column names to MSstats format
-#' 
-#' @param input Data frame
-#' @param quantificationColumn Name of intensity column
-#' @return Data frame with renamed columns
-#' @keywords internal
-.standardizeDIANNColumns <- function(input, quantificationColumn) {
-  input <- dplyr::rename_with(input, .fn = function(x) gsub("\\.", "", x))
-
-  # Standardize column names
-  clean_quant_col <- gsub("\\.", "", quantificationColumn)
-  old_names <- c('ProteinNames', 'StrippedSequence', 'ModifiedSequence',
-                 'PrecursorCharge', clean_quant_col, 'QValue',
-                 'PrecursorMz', 'FragmentIon', 'Run', 'ProductCharge')
-  new_names <- c('ProteinName', 'PeptideSequence', 'PeptideModifiedSequence',
-                 'PrecursorCharge', 'Intensity', 'DetectionQValue', 
-                 'PrecursorMz', 'FragmentIon', 'Run', 'ProductCharge')
-
-  current_names <- colnames(input)
-  names_to_rename <- intersect(current_names, old_names)
-
-  # Create a named vector for renaming in the format c(new_name = old_name)
-  new_names_subset <- new_names[match(names_to_rename, old_names)]
-  rename_map <- setNames(names_to_rename, new_names_subset)
-  rename_map <- rename_map[!is.na(names(rename_map))]
-
-  dplyr::rename(input, any_of(rename_map))
-}
-
-#' Filter data by Q-values
-#' 
-#' @param input Data frame
-#' @param MBR Boolean
-#' @param global_qvalue_cutoff Numeric
-#' @param qvalue_cutoff Numeric
-#' @param pg_qvalue_cutoff Numeric
-#' @return Filtered data frame
-#' @keywords internal
-.filterDIANNByQValues <- function(input, MBR, global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff) {
-  input <- dplyr::filter(input, DetectionQValue < global_qvalue_cutoff)
-
-  if (MBR) {
-    dplyr::filter(input, LibPGQValue < pg_qvalue_cutoff & LibQValue < qvalue_cutoff)
-  } else {
-    dplyr::filter(input, GlobalPGQValue < pg_qvalue_cutoff & GlobalQValue < qvalue_cutoff)
-  }
-}
-
-#' Finalize columns for output
-#' 
-#' @param input Data frame
-#' @return Data frame with final columns
-#' @keywords internal
-.finalizeDIANNColumns <- function(input) {
-  # Final column selection for MSstats format
-  msstats_cols <- c("ProteinName", "PeptideSequence", "PeptideModifiedSequence", "PrecursorCharge", 
-                    "FragmentIon", "ProductCharge", "Run", "Intensity")
-
-
-  # Add annotation columns if they exist
-  if ("Condition" %in% colnames(input)) msstats_cols <- c(msstats_cols, "Condition")
-  if ("BioReplicate" %in% colnames(input)) msstats_cols <- c(msstats_cols, "BioReplicate")
-
-  # Add IsotopeLabelType, assuming Light for DIANN
-  input$IsotopeLabelType <- "L"
-  msstats_cols <- c(msstats_cols, "IsotopeLabelType")
-
-  final_cols <- intersect(msstats_cols, colnames(input))
-  dplyr::select(input, all_of(final_cols))
+    .writeChunkToFile(input, output_path, pos)
+    NULL
 }
-
-#' Write chunk to file
-#' 
-#' @param input Data frame
-#' @param output_path Path to output file
-#' @param pos Chunk position
-#' @return NULL
-#' @keywords internal
-.writeDIANNChunk <- function(input, output_path, pos) {
-  # Write to file
-  if (!is.null(pos)) {
-    if (pos == 1) {
-      readr::write_csv(input, file = output_path, append = FALSE)
-    } else {
-      readr::write_csv(input, file = output_path, append = TRUE)
-    }
-  }
-}

R/clean_spectronaut.R

@@ -117,12 +117,6 @@ cleanSpectronautChunk = function(input, output_path,
   }
 
   input <- dplyr::select(input, select_cols)
-  if (!is.null(pos)) {
-    if (pos == 1) {
-      readr::write_csv(input, file = output_path, append = FALSE)
-    } else {
-      readr::write_csv(input, file = output_path, append = TRUE)
-    }
-  }
+  .writeChunkToFile(input, output_path, pos)
   NULL
 }

R/converters.R

@@ -159,8 +159,9 @@ bigSpectronauttoMSstatsFormat <-  function(input_file, output_file_name,
 #'
 #' @inheritParams MSstatsPreprocessBig
 #' @param MBR True if analysis was done with match between runs.
-#' @param quantificationColumn Use 'Fragment.Quant.Corrected'(default) column for quantified intensities for DIANN 1.8.x.
+#' @param quantificationColumn Use 'FragmentQuantCorrected'(default) column for quantified intensities for DIANN 1.8.x.
 #' Use 'FragmentQuantRaw' for quantified intensities for DIANN 1.9.x. 
+#' Use 'auto' for quantified intensities for DIANN 2.0+
 #'
 #' @export
 #'
@@ -171,7 +172,7 @@ bigDIANNtoMSstatsFormat <- function(input_file,
                                     output_file_name,
                                     backend,
                                     MBR = TRUE,
-                                    quantificationColumn = "Fragment.Quant.Corrected",
+                                    quantificationColumn = "FragmentQuantCorrected",
                                     max_feature_count = 100,
                                     filter_unique_peptides =  FALSE,
                                     aggregate_psms =  FALSE,

R/utils.R

@@ -0,0 +1,17 @@
+#' Write chunk to file
+#' 
+#' @param input Data frame
+#' @param output_path Path to output file
+#' @param pos Chunk position
+#' @return NULL
+#' @keywords internal
+.writeChunkToFile <- function(input, output_path, pos) {
+    # Write to file
+    if (!is.null(pos)) {
+        if (pos == 1) {
+            readr::write_csv(input, file = output_path, append = FALSE)
+        } else {
+            readr::write_csv(input, file = output_path, append = TRUE)
+        }
+    }
+}